Table_1_Determinants of bacterial and fungal microbiota in Finnish home dust: Impact of environmental biodiversity, pets, and occupants.XLSX

The indoors is where many humans spend most of their time, and are strongly exposed to indoor microbiota, which may have multifaceted effects on health. Therefore, a comprehensive understanding of the determinants of indoor microbiota is necessary. We collected dust samples from 295 homes of families with young children in the Helsinki region of Finland and analyzed the bacterial and fungal composition based on the 16S rRNA and ITS DNA sequences. Microbial profiles were combined with extensive survey data on family structure, daily life, and physical characteristics of the home, as well as additional external environmental information, such as land use, and vegetational biodiversity near the home. Using permutational multivariate analysis of variance we explained 18% of the variation of the relative abundance between samples within bacterial composition, and 17% of the fungal composition with the explanatory variables. The fungal community was dominated by the phyla Basidiomycota, and Ascomycota; the bacterial phyla Proteobacteria, Firmicutes, Cyanobacteria, and Actinobacteria were dominant. The presence of dogs, multiple children, and firewood were significantly associated with both the fungal and bacterial composition. Additionally, fungal communities were associated with land use, biodiversity in the area, and the type of building, while bacterial communities were associated with the human inhabitants and cleaning practices. A distinction emerged between members of Ascomycota and Basidiomycota, Ascomycota being more abundant in homes with greater surrounding natural environment, and potential contact with the environment. The results suggest that the fungal composition is strongly dependent on the transport of outdoor environmental fungi into homes, while bacteria are largely derived from the inhabitants.</p

Presentation_1_Determinants of bacterial and fungal microbiota in Finnish home dust: Impact of environmental biodiversity, pets, and occupants.PPTX

The indoors is where many humans spend most of their time, and are strongly exposed to indoor microbiota, which may have multifaceted effects on health. Therefore, a comprehensive understanding of the determinants of indoor microbiota is necessary. We collected dust samples from 295 homes of families with young children in the Helsinki region of Finland and analyzed the bacterial and fungal composition based on the 16S rRNA and ITS DNA sequences. Microbial profiles were combined with extensive survey data on family structure, daily life, and physical characteristics of the home, as well as additional external environmental information, such as land use, and vegetational biodiversity near the home. Using permutational multivariate analysis of variance we explained 18% of the variation of the relative abundance between samples within bacterial composition, and 17% of the fungal composition with the explanatory variables. The fungal community was dominated by the phyla Basidiomycota, and Ascomycota; the bacterial phyla Proteobacteria, Firmicutes, Cyanobacteria, and Actinobacteria were dominant. The presence of dogs, multiple children, and firewood were significantly associated with both the fungal and bacterial composition. Additionally, fungal communities were associated with land use, biodiversity in the area, and the type of building, while bacterial communities were associated with the human inhabitants and cleaning practices. A distinction emerged between members of Ascomycota and Basidiomycota, Ascomycota being more abundant in homes with greater surrounding natural environment, and potential contact with the environment. The results suggest that the fungal composition is strongly dependent on the transport of outdoor environmental fungi into homes, while bacteria are largely derived from the inhabitants.</p

Bacterial groups associated with the treatments.

<p>Genus-level bacterial groups significantly associated with at least one of the treatments (<i>L</i>. <i>rhamnosus</i> GG or antibiotics), clustered based on their response profile to the <i>L</i>. <i>rhamnosus</i> GG and antibiotics. The average total abundances (± standard error) of the clusters in the different treatment groups are shown in the barplots. Significance of the difference from the control group (based on negative binomial models) are indicated by the asterisks: * p<0.05; ** p<0.01; *** p<0.001.</p

Microbiota composition by treatment group.

<p>Composition in the placebo group (panel A) and <i>L</i>. <i>rhamnosus</i> GG group (panel B). The component scores were calculated using principal coordinates analysis of the Pearson correlation distances in the species-level data. All samples were included in the same analysis, and the groups are shown in different panels for clarity.</p

Antibiotic use before, during, and after the intervention in the LGG group (N = 124) and placebo group (N = 107).

<p>Antibiotic use before, during, and after the intervention in the LGG group (N = 124) and placebo group (N = 107).</p

Flow-chart of the study cohort.

<p>Flow-chart of the study cohort.</p

Data_Sheet_3_Bacteroides abundance drives birth mode dependent infant gut microbiota developmental trajectories.XLSX

Background and aimsBirth mode and other early life factors affect a newborn's microbial colonization with potential long-term health effects. Individual variations in early life gut microbiota development, especially their effects on the functional repertoire of microbiota, are still poorly characterized. This study aims to provide new insights into the gut microbiome developmental trajectories during the first year of life.MethodsOur study comprised 78 term infants sampled at 3 weeks, 3 months, 6 months, and 12 months (n = 280 total samples), and their mothers were sampled in late pregnancy (n = 50). Fecal DNA was subjected to shotgun metagenomic sequencing. Infant samples were studied for taxonomic and functional maturation, and maternal microbiota was used as a reference. Hierarchical clustering on taxonomic profiles was used to identify the main microbiota developmental trajectories in the infants, and their associations with perinatal and postnatal factors were assessed.ResultsIn line with previous studies, infant microbiota composition showed increased alpha diversity and decreased beta diversity by age, converging toward an adult-like profile. However, we did not observe an increase in functional alpha diversity, which was stable and comparable with the mother samples throughout all the sampling points. Using a de novo clustering approach, two main infant microbiota clusters driven by Bacteroidaceae and Clostridiaceae emerged at each time point. The clusters were associated with birth mode and their functions differed mainly in terms of biosynthetic and carbohydrate degradation pathways, some of which consistently differed between the clusters for all the time points. The longitudinal analysis indicated three main microbiota developmental trajectories, with the majority of the infants retaining their characteristic cluster until 1 year. As many as 40% of vaginally delivered infants were grouped with infants delivered by C-section due to their clear and persistent depletion in Bacteroides. Intrapartum antibiotics, any perinatal or postnatal factors, maternal microbiota composition, or other maternal factors did not explain the depletion in Bacteroides in the subset of vaginally born infants.ConclusionOur study provides an enhanced understanding of the compositional and functional early life gut microbiota trajectories, opening avenues for investigating elusive causes that influence non-typical microbiota development.</p

Incidence of antibiotic use (%) in the placebo and <i>L</i>. <i>rhamnosus</i> GG groups.

<p>The dashed line shows the end of the intervention. The p-values indicate the significance of the difference (indicated by the arrows at different time points, based on survival regression models) in antibiotic use between the groups at the end of the intervention, 1 year after the intervention, 2 years after the intervention, and 2.7 years after the intervention.</p

Characteristics of the study cohort (N = 231).

<p>Control = received neither antibiotics nor <i>L</i>. <i>rhamnosus</i> GG during the intervention; Pen = received penicillin; Mac = received macrolide and/or penicillin; LGG = received <i>L</i>. <i>rhamnosus</i> GG but no antibiotics; Pen+LGG = received <i>L</i>. <i>rhamnosus</i> GG and penicillin; Mac+LGG = received <i>L</i>. <i>rhamnosus</i> GG and macrolide and/or penicillin.</p

Microbiota diversity and stability by treatment group.

<p>Diversity after the intervention was calculated as the Inverse Simpson index (panel A), and microbiota stability as the Pearson correlation between the pre- and post-intervention sample (panel B). Significant differences based on analysis of variance are shown.</p